How Threat Detection Engineer Builds MITRE ATT&CK Coverage in 48 Hours

Your CISO walks into the Monday standup with a simple request: we need coverage for the latest MITRE ATT&CK techniques targeting cloud identity systems, and we need it operational by Wednesday morning. Two days. No room for botched deployments or alert storms that bury your SOC team.

This is the reality of modern threat detection engineering. The median security operations team now manages between 50 and 150 active SIEM rules, but CardinalOps' 2025 State of SIEM report found that enterprise SIEMs only maintain detection coverage for 22% of applicable MITRE ATT&CK techniques. That's a sobering gap, and closing it quickly—without introducing noise—separates competent detection engineers from exceptional ones.

Here's how our Threat Detection Engineer builds production-ready MITRE ATT&CK coverage in 48 hours using detection-as-code pipelines, automated testing, and intelligent alert tuning.

Hour 0–6: Scope and Threat Intelligence Mapping

Speed starts with surgical focus. You can't instrument everything, so you target the techniques that matter most to your threat landscape.

First, identify your coverage gaps. Pull your existing SIEM rule inventory and map every active detection to its corresponding MITRE ATT&CK technique. Most modern SIEM platforms—Elastic, Splunk, Microsoft Sentinel—support ATT&CK tagging natively. If yours doesn't, you're already behind.

Cross-reference your gaps against recent threat intelligence. In the 2025 MITRE ATT&CK Enterprise Evaluation, leading vendors like CrowdStrike and Cybereason demonstrated 100% detection coverage across tested techniques, but those evaluations focus on endpoint telemetry. Your SIEM needs cloud-native detections too.

Elastic's 2025 State of Detection Engineering report documented the development of 23 new SIEM rules specifically for Cloud Credential Access techniques across AWS, Okta, Microsoft 365, and endpoint environments. That's your blueprint.

Prioritise based on:

• Threat actor TTPs actively targeting your sector
• High-value assets in your environment (crown jewels analysis)
• Regulatory requirements (NIS2, DORA, SOC 2 Type II)
• Observability gaps where you have logs but no detections

By hour six, you should have a prioritised list of 8–12 MITRE ATT&CK techniques and the data sources required to detect them. Document your rationale. You'll need it when leadership asks why you focused on Credential Access over Initial Access.

Hour 6–18: Rule Development and Detection-as-Code Implementation

Now the real engineering begins. Traditional detection engineering involves writing rules directly in your SIEM's query language, testing manually, and hoping nothing breaks in production. That approach is dead.

Modern detection engineers use detection-as-code pipelines. Your rules live in version control—Git, not a SIEM console. Every detection is a structured file (YAML, TOML, or JSON) that defines the logic, metadata, MITRE mappings, severity, and test cases in a single artefact.

Here's the workflow:

Write modular, testable detections

Start with a detection template. If you're working in Sigma (the universal SIEM rule format), your rule might look like this:

title: AWS IAM Policy Modified for Privilege Escalation
status: stable
logsource:
  product: aws
  service: cloudtrail
detection:
  selection:
    eventName:
      - PutUserPolicy
      - PutGroupPolicy
      - PutRolePolicy
  condition: selection

Each rule targets a single technique. T1098.001 (Account Manipulation: Additional Cloud Credentials) and T1078.004 (Valid Accounts: Cloud Accounts) often overlap; write separate detections for each.

Include tuning parameters from day one. Alert thresholds, time windows, and allowlists should be configurable without editing core logic. This prevents future alert fatigue.

Build automated testing into every rule

Detection-as-code pipelines run automated tests before any rule touches production. Create test datasets—both malicious samples that should fire and benign activity that shouldn't.

Your CI/CD pipeline (GitHub Actions, GitLab CI, Jenkins) validates:

• Syntax correctness
• Schema compliance
• Test case pass/fail
• MITRE ATT&CK mapping accuracy
• Performance impact estimates

If a rule fails testing, the pipeline blocks the merge. No exceptions.

Map to MITRE ATT&CK comprehensively

Every detection must declare its ATT&CK coverage explicitly. Use the technique ID, tactic, and sub-technique where applicable. This metadata powers your coverage heatmap and demonstrates regulatory compliance during audits.

By hour 18, you've written 8–12 production-quality detections, each with automated tests and MITRE mappings. They're sitting in a Git repository, not yet deployed.

Hour 18–36: Integration, Deployment, and SOC Enablement

You can't just push rules into production and walk away. Deployment requires coordination with your SOC, validation in a staging environment, and runbook creation for every new alert type.

Stage and validate in pre-production

Deploy your new rules to a staging SIEM instance that mirrors production data. Run them for at least 4–6 hours against live telemetry. Watch for:

• False positive rate: Anything above 5% needs immediate tuning
• Alert volume: A single rule generating more than 10 alerts per hour will drown your analysts
• Query performance: Rules that take longer than 15 seconds to execute create SIEM lag

Tune aggressively. Add exclusions for known benign behaviour. Raise thresholds if necessary. The goal is actionable alerts, not alert volume.

Create SOC playbooks

Every new detection requires a response playbook. Your analysts need to know:

• What the alert indicates (threat context)
• Initial triage steps (log queries, enrichment sources)
• Escalation criteria (when to page incident response)
• Remediation actions (credential resets, account lockdowns)

Template these. Our Threat Detection Engineer generates playbooks automatically from detection metadata, saving 2–3 hours per rule.

Deploy via pipeline

Merge your branch. Your CI/CD pipeline automatically:

1. Runs final validation tests
2. Converts Sigma rules to platform-specific queries (SPL for Splunk, KQL for Sentinel, ES|QL for Elastic)
3. Deploys to production SIEM
4. Updates your MITRE ATT&CK coverage dashboard
5. Notifies the SOC team via Slack or Microsoft Teams

By hour 36, your detections are live.

Hour 36–48: Monitoring, Tuning, and Coverage Validation

The first 12 hours in production are critical. This is where brittle rules reveal themselves and where tuning separates noise from signal.

Monitor alert quality obsessively

Track every triggered alert. Are analysts investigating them? Are they marking them as false positives? If your true positive rate drops below 40%, something's wrong.

Use detection health metrics:

• Mean time to investigate (MTTI): How long until an analyst opens the alert?
• Mean time to triage (MTTT): How long until they classify it as true/false positive?
• Detection efficacy rate: Percentage of alerts that result in genuine investigations

Tune based on real-world feedback

SOC feedback is gold. If analysts report that a specific rule generates false positives for service accounts, add those accounts to an allowlist. If an alert fires during a known maintenance window, implement a time-based suppression.

Tuning is iterative. The 2025 State of Detection Engineering at Elastic emphasised that mature detection engineering teams revisit every rule quarterly, adjusting for environmental changes and emerging TTPs.

Validate coverage and report upward

By hour 48, you should have:

• 8–12 new SIEM rules in production
• Documented MITRE ATT&CK coverage gains (e.g., "increased T1078 coverage from 35% to 85%")
• SOC playbooks for each detection
• Initial alert metrics (volume, false positive rate, investigation outcomes)

Generate a coverage heatmap. Tools like MITRE ATT&CK Navigator visualise which techniques you now detect, which remain gaps, and where your defences are strongest. Present this to your CISO on Wednesday morning.

"Modern detection engineering isn't about writing more rules. It's about writing the right rules, testing them rigorously, and deploying them without breaking your SOC." — CardinalOps, 2025 State of SIEM Report

What This Actually Costs

If you're hiring a human threat detection engineer in the UK, expect to pay between £60,000 and £90,000 annually for mid-level talent, according to 2026 UK cybersecurity salary data. Senior detection engineers with SIEM expertise and detection-as-code pipeline experience command £90,000–£120,000+.

In the UAE, cybersecurity analysts and detection engineers earn between AED 180,000 and AED 300,000 annually (approximately £40,000–£65,000), with mid-level professionals in Dubai earning AED 15,000–30,000 per month.

Freelance detection engineers bill £500–£800 per day for SIEM rule development and threat hunting projects. A 48-hour sprint executed by a contractor costs £1,000–£1,600 before overhead.

Compare that to an AI agent that executes the same workflow—scoping, rule development, testing, deployment, tuning—without fatigue, handoffs, or context-switching. Check out our pricing to see how AI-native detection engineering stacks up.

Final Thoughts

Building MITRE ATT&CK coverage in 48 hours isn't about cutting corners. It's about disciplined engineering: clear scoping, modular rule design, automated testing, and obsessive tuning.

The security operations landscape has shifted. Detection-as-code pipelines, MITRE-native tooling, and AI-augmented threat intelligence make it possible to deploy enterprise-grade detections faster than ever. But speed without rigour produces alert noise, not security outcomes.

The best detection engineers—human or AI—understand that coverage is a continuous process. Your threat landscape evolves weekly. Your detections must evolve with it.

Explore how The Agency's engineering agents can accelerate your security operations, or dive into our solutions for AI-native detection engineering and SIEM automation.